EE263 - Introduction to Linear Dynamical Systems

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Provider
Stanford
Cost
Free
Certificate
No certificate
Format
Self-paced
Language
English
Subjects
Mathematics
Source
Stanford Online
Last verified
14 Sep 2026

Introduction to Linear Dynamical Systems is Stephen Boyd's EE263, an applied linear algebra course with applications to circuits, signal processing, communications and control, released in full on Stanford Engineering Everywhere. The topics run from least-squares approximation and least-norm solutions through symmetric matrices, matrix norms and the singular value decomposition, eigenvalues and their dynamical interpretation, the matrix exponential, stability and asymptotic behaviour, to multi-input multi-output systems, convolution and transfer-matrix descriptions, control, reachability, state transfer, observability and least-squares state estimation. The page asks for exposure to linear algebra and matrices along with differential equations, the Laplace transform and transfer functions; exposure to control systems, circuits or signals and systems is helpful but not required.

SEE provides 20 lecture videos with transcripts, a full set of lecture handouts organised by topic, assignments, exams and software. As with all SEE courses, access is free without registration under a CC BY-NC-SA 4.0 licence, and there is no credit, certificate or graded feedback.

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What you’ll learn

  • Solve over-determined and under-determined systems with least-squares and least-norm methods
  • Use symmetric matrices, matrix norms and the singular value decomposition
  • Interpret eigenvalues, eigenvectors and the matrix exponential dynamically
  • Analyse stability and asymptotic behaviour of linear dynamical systems
  • Describe multi-input multi-output systems with impulse, step and transfer matrices
  • Apply reachability, observability and least-squares state estimation

Who it’s for

Students who know basic linear algebra and differential equations and want the matrix tools behind control, signal processing and estimation.

Source: Stanford Online (opens in a new tab) · Verified · Report a change

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